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The features, dimensions and organisation of internal elements in freestall dairy barns are decisive in determining the layout design.
In this study, we utilized the maximum relevance minimum redundancy mRMR algorithm, which reduces the features' dimensions by selecting the most relevant features while removing the redundant ones.
This paper presents a comprehensive framework for product remanufacturability assessment, remanufacturing process planning, and a systematic approach to design for disassembly for remanufacturing based on product design information available in CAD models, e.g., bill of materials, mating features, dimensions and tolerances, tools and accessories, etc.
From a technical standpoint, this approach is broadly called dimensionality reduction, where a given data set with too many features (dimensions) can be reduced into a map where only relevant, meaningful information is represented.
We endowed our memory with a set of algorithms that insure flexibility, enabling it to add/delete attractors as well as features (dimensions) without need to retrain the network.
To study them, we inevitably had to reduce the actual and diverse reality to a relatively small number of features, dimensions, and categories.
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This shows clearly that one analytical feature (dimension 1) can solve the problem almost perfectly, whereas a full set of generic features (dimension 100) cannot.
Features dimension significantly affects ANN training performance as well as generalization ability.
Hence, we utilize the principal component analysis (PCA) approach to reduce the features' dimension [31].
As discussed above, the features dimension determined by ∆T according to (3) is highly related to the prediction precision.
Taking voltage trajectory of generator bus as an example, the principle of determining preferable features dimension is detailed as follows.
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